Anthropic has launched its own drug discovery programs to address diseases that traditional pharmaceutical companies consider unprofitable. The company plans to focus on early-stage research for neglected conditions, leveraging its AI tools to improve drug development processes. The initiative aligns with Anthropic's nonprofit mission and aims to enhance its AI models and tools for broader industry use. The announcement was made during an event for its new science AI tool, 'Claude Science,' which showcased how AI can accelerate medical research. A researcher at UCSF used Claude Science to identify a viral contamination in minutes, a task that had previously taken over a year to detect. The company also stated that Claude analyzed 100 rare genetic diseases in under an hour, flagging 32 candidates for further screening. Source: thedecoder

Novartis CEO Vas Narasimhan highlighted that developing a drug candidate from research to approval currently takes about twelve years. He attributed delays to three categories: information latency, operational latency, and biological latency. New AI tools and models could significantly reduce the first two categories, which account for roughly 40% of total development time. Biological latency, involving animal testing, cell models, and human trials, is expected to remain largely unchanged. Narasimhan also suggested that success rates could double from 8% to 16% with better safety predictions and optimized molecular properties, though the impact of improved patient selection remains uncertain. Source: thedecoder

Other AI companies are also expanding into healthcare. Deepmind CEO Demis Hassabis co-founded Isomorphic Labs with Alphabet to apply AI to drug discovery, while Google Deepmind's AlphaFold has been a key example of AI in biology. OpenAI launched ChatGPT Health in early 2026, allowing users to connect medical records and wellness apps. However, experts caution that AI's role in clinical settings for diagnoses and treatment plans remains complex and requires careful oversight. Source: thedecoder